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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Quality assurance and validation techniques 3. Data standardization and transformation - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data collection methods and sources 2. Data formats and storage systems 3. Data integration and merging
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization principles and best practices 1. Audience-focused presentation 2. Choosing appropriate chart types 3. Color, layout and clarity - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices
SQL and Database Integration	10%	- Python-database connectivity 1. Error handling and best practices 2. Connecting to SQLite, MySQL or PostgreSQL 3. Executing queries and retrieving results - Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

A. Scatter plots
B. Histogram analysis
C. Box plots
D. Correlation matrix

Answer: A,D

NEW QUESTION # 86
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. id() function to retrieve unique object identifiers
B. == operator for checking value equivalence
C. str() function to convert data formats
D. is operator for memory-level comparison

Answer: A,D

NEW QUESTION # 87
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Encrypting the database connection
B. Escaping quotes manually in SQL strings
C. Using parameterized queries
D. Closing the database cursor after each query

Answer: C

NEW QUESTION # 88
What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Unstructured data contains only numeric values
B. Structured data cannot be stored in databases
C. Structured data follows a consistent schema or format
D. Unstructured data cannot be stored or analyzed

Answer: C

NEW QUESTION # 89
Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.isin()
B. df.clip()
C. df.sort_values()
D. df.sample()

Answer: A

NEW QUESTION # 90
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